Data Management & Data Governance 102: Differences between the DAMA-DMBOK and the DCAM approaches
CHECK OUT THE PREVIOUS ARTICLE OF THIS SERIES: Data Management & Data Governance 101: The Yin and Yang Duality
Although the terms ‘data governance’ and ‘data management’ are used in different industry reference guides, such as DAMA-DMBOK1 and DCAM2, in different contexts and sources, they have different definitions, meaning, and relationships with each other.3I chose these guides for comparison as they are the most trustworthy and most frequently used sources in the data management professional community.
For those who would like to have some ‘fun’ and jump into the jungle of definitions provided by other prominent data-related organizations, you are welcome to take a look at the attachment in the end of this article.
In this article, I aim to explore the deeper discrepancies between DAMA-DMBOK and DCAM in the approaches and to discuss possible reasons for that.
You might ask: why is it so important? Assume, two data management specialists talk to each other about data governance. One bases his opinion on the DAMA-DMBOK model, and the other – on DCAM. If they do not align their language upfront, their conversation will bring the same results as those achieved by the builders of the tower of Babel.
Let us have a look at the key discrepancies between DAMA-DMBOK and DCAM data management and data governance models.
Discrepancy no. 1: The nature of building blocks of data management models
According to the DAMA-DMBOK model, data management is a business function4, which scope is made up of different KnowledgeAreas5. Unfortunately, I could not find any definition of ‘business function’ in any of DAMA’s publications. DAMA-DMBOK uses the DAMA Environmental Factors Hexagon model to describe the Knowledge Area. So, I assume that the key components such as ‘people, process, and technology’(6) and their relationships are key components of the business function in the DAMA context. Yet, these are just my assumptions.
DCAM bases their model on the concept of ‘capability’. I could not find any definition of what ‘capability’ is and which components constitute a ‘capability’. ‘The Data Management Strategy (DMS) is Specified and Shared’(7) is one of the examples of a DCAM capability. Such a representation of a business capability does not correspond to a definition of a business capability provided by the Open Group: ‘a particular ability or capacity that a business may possess or exchange to achieve a specific purpose or outcome. Critically, a business capability delineates what a business does without attempting to explain how, why, or where the business uses the capability. […] The correct naming convention involves expressing the business capability as a noun (“this is what we do”) as opposed to a verb […]’(8).
So, the DCAM ‘capability’ clearly does not fit the definition and a ‘capability’ definition remains a mystery.
Conclusion: DAMA-DMBOK and DCAM base their data management models on the concepts of ‘Knowledge Area’ and ‘Capability’ correspondingly. These concepts are not clearly defined and not compatible with each other.
Discrepancy no. 2: The list of data management model building blocks and their content
Continuing with further comparison, let us compare the components that constitute DAMA-DMBOK and DCAM data management models.
DAMA-DMBOK represents data management in the form of 11 Knowledge Areas. In Figure 1 you can see the famous DAMA Wheel(9).
The latest version (v2) of the DCAM model includes 7 components:(10)
You can see that some components have similar names, such as ‘Data Governance’, ‘Data Architecture’, ‘Data Quality (Management)’. Some of them are of similar nature: for example, DCAM’s Data and Technology Architecture matches certain Knowledge Areas of DAMA-DMBOK, such as Data Integration & Interoperability, Data Warehousing & Business Intelligence. The rest of the components differ.
The differences go deeper into the content and deliverables of the data management components. Even if both models: DAMA-DMBOK and DCAM, has a component with the same name, this component has quite different meaning in the models. Have a look, for example, at the component ‘data governance’.
DAMA-DMBOK considers data (management) strategy, business case, data management programs as deliverables of the data governance function. DCAM recognizes them as separate components of data management11.
Conclusion: components of the data management model of DAMA-DMBOK and DCAM are only similar to some extent and the nature of this conceptual difference is not clear.
Discrepancy no.3: The relationship between data management and IT function
DAMA-DMBOK in its first edition clearly stated that data management professional organization was a part of Information Technology (IT) organization.12 The DAMA-DMBOK model of data management fully reflects this assumption and did not change in the second edition. I recognize this as the fact that DAMA-DMBOK takes a look at data management from the viewpoint of the enterprise.
Founder - DojiQL | Redeeming Your Time
8moJust bought your ebook. It so good to have access to framework under $100 as opposed to the thousand dollar memberships others insist on 😁
Data Management, Data Architecture, Data Governance, Data Privacy
4yGreat read , thank you.
Good data management makes the difference. Good data governance is key!
5yVery interesting, thank you. It shows once more why it is so important to clarify the differences between data governance and data management in each data management workshop with customers. Personally I believe that both, data management (especially master data management) and data governance are involving each other. One can´t "do" data management without data governance and data governance always will result in some sort of data management. So I go with you in the Yin-Yang dualism.
Head of Technology Alliances - Solidatus
5yHappy to offer time and resources to use #Solidatus to compare and contrast